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Introduction
Essentially in all aspect of daily life, technology is interwoven and embedded. Such as network and other communication devices are used for both educational and personal purposes. Whereas the inclination of the internet for citizens’ empowerment is neither straightforward nor unidirectional. One study examines that 83% of homes in Canada have access to the internet and out of these 97% report having high-speed internet ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"BhlMHfrG","properties":{"formattedCitation":"(Kotsopoulos et al., 2017)","plainCitation":"(Kotsopoulos et al., 2017)","noteIndex":0},"citationItems":[{"id":357,"uris":["http://zotero.org/users/local/orkqtrjP/items/KEHKITPN"],"uri":["http://zotero.org/users/local/orkqtrjP/items/KEHKITPN"],"itemData":{"id":357,"type":"article-journal","title":"A Pedagogical Framework for Computational Thinking","container-title":"Digital Experiences in Mathematics Education","page":"154-171","volume":"3","issue":"2","source":"Springer Link","abstract":"Our goal in this paper is to propose a Computational Thinking Pedagogical Framework (CTPF), developed from constructionism and social-constructivism theories. CTPF includes four pedagogical experiences: (1) unplugged, (2) tinkering, (3) making, and (4) remixing. Unplugged experiences focus on activities implemented without the use of computers. Tinkering experiences primarily involve activities that take things apart and engaging in changes and/or modifications to existing objects. Making experiences involve activities where constructing new objects is the primary focus. Remixing refers to those experiences that involve the appropriation of objects or components of objects for use in other objects or for other purposes. Objects can be digital, tangible, or even conceptual. These experiences reflect distinct yet overlapping CT experiences which are all proposed to be necessary in order for students to fully experience CT. In some cases, particularly for novices and depending on the concepts under exploration, a sequential approach to these experiences may be helpful.","DOI":"10.1007/s40751-017-0031-2","ISSN":"2199-3254","journalAbbreviation":"Digit Exp Math Educ","language":"en","author":[{"family":"Kotsopoulos","given":"Donna"},{"family":"Floyd","given":"Lisa"},{"family":"Khan","given":"Steven"},{"family":"Namukasa","given":"Immaculate Kizito"},{"family":"Somanath","given":"Sowmya"},{"family":"Weber","given":"Jessica"},{"family":"Yiu","given":"Chris"}],"issued":{"date-parts":[["2017",8,1]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Kotsopoulos et al., 2017). This has given rise to the data-analytic environment and favors a political campaign. Big data is often obtained from the internet with computational tools and analytics. Earlier, large data-sets were primarily obtained from surveys and on voluntarily basis questions were answered. Compared to surveys, political computation’s results are measured quickly and in real time in the delivery.
Discussion
Computational Thinking (CT) or Computational Practice (CP) is an approach for solving big data problems by developing a system for the understanding of human behavior ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"PMFeQoAt","properties":{"formattedCitation":"(Kotsopoulos et al., 2017)","plainCitation":"(Kotsopoulos et al., 2017)","noteIndex":0},"citationItems":[{"id":357,"uris":["http://zotero.org/users/local/orkqtrjP/items/KEHKITPN"],"uri":["http://zotero.org/users/local/orkqtrjP/items/KEHKITPN"],"itemData":{"id":357,"type":"article-journal","title":"A Pedagogical Framework for Computational Thinking","container-title":"Digital Experiences in Mathematics Education","page":"154-171","volume":"3","issue":"2","source":"Springer Link","abstract":"Our goal in this paper is to propose a Computational Thinking Pedagogical Framework (CTPF), developed from constructionism and social-constructivism theories. CTPF includes four pedagogical experiences: (1) unplugged, (2) tinkering, (3) making, and (4) remixing. Unplugged experiences focus on activities implemented without the use of computers. Tinkering experiences primarily involve activities that take things apart and engaging in changes and/or modifications to existing objects. Making experiences involve activities where constructing new objects is the primary focus. Remixing refers to those experiences that involve the appropriation of objects or components of objects for use in other objects or for other purposes. Objects can be digital, tangible, or even conceptual. These experiences reflect distinct yet overlapping CT experiences which are all proposed to be necessary in order for students to fully experience CT. In some cases, particularly for novices and depending on the concepts under exploration, a sequential approach to these experiences may be helpful.","DOI":"10.1007/s40751-017-0031-2","ISSN":"2199-3254","journalAbbreviation":"Digit Exp Math Educ","language":"en","author":[{"family":"Kotsopoulos","given":"Donna"},{"family":"Floyd","given":"Lisa"},{"family":"Khan","given":"Steven"},{"family":"Namukasa","given":"Immaculate Kizito"},{"family":"Somanath","given":"Sowmya"},{"family":"Weber","given":"Jessica"},{"family":"Yiu","given":"Chris"}],"issued":{"date-parts":[["2017",8,1]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Kotsopoulos et al., 2017). These drawn behaviors are essential to concepts. By using principles from computer sciences for the guidance of metaphorical frameworks and structures, Computational Thinking is analyzed algorithmically. The data for analyzing mostly comes from the public sphere, whose rational arguments concern the public on the issues of governance.
Computational Politics denotes to the application of computational methods to a large number of data-sets derived from the internet and other sources for conducting persuasion and mobilization, and outreach in the service of opposing or furthering a candidate, legislation or policy ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"VS4zdVxp","properties":{"formattedCitation":"(Shinu, 2018)","plainCitation":"(Shinu, 2018)","noteIndex":0},"citationItems":[{"id":358,"uris":["http://zotero.org/users/local/orkqtrjP/items/ULVZRWRT"],"uri":["http://zotero.org/users/local/orkqtrjP/items/ULVZRWRT"],"itemData":{"id":358,"type":"post-weblog","title":"Computational Politics: when democracy is all about the majority of data.","container-title":"Medium","abstract":"They’re both games, Computer Science and Politics. A bit of a gamble, a bit of logic, a bit of manipulation, a bit of opportunistic…","URL":"https://medium.com/redefyn/computational-politics-when-democracy-is-all-about-the-majority-of-data-21e0412ca1d3","shortTitle":"Computational Politics","author":[{"family":"Shinu","given":"Ashmy Achu"}],"issued":{"date-parts":[["2018",12,9]]},"accessed":{"date-parts":[["2019",4,9]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Shinu, 2018). The data-points are purchased from Facebook and Amazon could be mined by interested political parties and turned into accurate models of definite individuals.
With the advent of computational politics, the finance for campaigns, privacy and governance went low. While the data used in elections needs to be an oversight as the data itself is generated by the people itself and increasingly being used at them. Such as Obama’s 2008 and 2012 campaigns were distinguished for the data-driven culture. The increase in digital platforms permitted integrating real-time experimentation into a conveyance of the political message. This method included the development of multiple versions of messages or screens to be delivered separately to certain random groups.
In this regard, behavioral science models guide how to influence and persuade people by moving into a particular action ADDIN ZOTERO_ITEM CSL_CITATION {"citationID":"zPT0JCNa","properties":{"formattedCitation":"(Tufekci, 2014)","plainCitation":"(Tufekci, 2014)","noteIndex":0},"citationItems":[{"id":361,"uris":["http://zotero.org/users/local/orkqtrjP/items/HGGT8ZCJ"],"uri":["http://zotero.org/users/local/orkqtrjP/items/HGGT8ZCJ"],"itemData":{"id":361,"type":"article-journal","title":"Engineering the public: Big data, surveillance and computational politics","container-title":"First Monday","volume":"19","issue":"7","source":"firstmonday.org","abstract":"Digital technologies have given rise to a new combination of big data and computational practices which allow for massive, latent data collection and sophisticated computational modeling, increasing the capacity of those with resources and access to use these tools to carry out highly effective, opaque and unaccountable campaigns of persuasion and social engineering in political, civic and commercial spheres. I examine six intertwined dynamics that pertain to the rise of computational politics: the rise of big data, the shift away from demographics to individualized targeting, the opacity and power of computational modeling, the use of persuasive behavioral science, digital media enabling dynamic real-time experimentation, and the growth of new power brokers who own the data or social media environments. I then examine the consequences of these new mechanisms on the public sphere and political campaigns.","URL":"https://firstmonday.org/ojs/index.php/fm/article/view/4901","DOI":"10.5210/fm.v19i7.4901","ISSN":"13960466","shortTitle":"Engineering the public","language":"en","author":[{"family":"Tufekci","given":"Zeynep"}],"issued":{"date-parts":[["2014",7,2]]},"accessed":{"date-parts":[["2019",4,10]]}}}],"schema":"https://github.com/citation-style-language/schema/raw/master/csl-citation.json"} (Tufekci, 2014). The development of a deeper model is central to altering the ability to analyze data, test data and turn the political behavior. The political campaign hopes to garner votes by identifying voters and individually targets them with tactics designed for their personal vulnerabilities and weaknesses. This all is done in a way that would not be visible to a broader public, such as through Facebook and Twitter ads.
In the Harrington model of political computation, there are two types of candidates, those who adopt the model and those who do not and are ideologues. As the competitors are scanned up, the number of survivors get decreased. The Harrington Model evaluates which form of candidates survive after a change in the exogenous environment over time. Therefore, Harrington model can be viewed as pie-splitting. Moreover, the voters have lexicographic preferences in the model, while rigid candidates lose more often.
Conclusion
The political computational results are often high in quality and increasing their impacts in contemporary elections campaigns. There is also an increase in interest among political science as it offers a bridge between scientific aspirations and political science itself. In this regard, the agent-based model in computational politics is an emergent model, developed by both political scientists and economists. This model collects and addresses the question related to electoral computation, international political issues, and institutional design and performance.
References
ADDIN ZOTERO_BIBL {"uncited":[],"omitted":[],"custom":[]} CSL_BIBLIOGRAPHY Kotsopoulos, D., Floyd, L., Khan, S., Namukasa, I. K., Somanath, S., Weber, J., & Yiu, C. (2017). A Pedagogical Framework for Computational Thinking. Digital Experiences in Mathematics Education, 3(2), 154–171. https://doi.org/10.1007/s40751-017-0031-2
Shinu, A. A. (2018, December 9). Computational Politics: when democracy is all about the majority of data. Retrieved April 9, 2019, from Medium website: https://medium.com/redefyn/computational-politics-when-democracy-is-all-about-the-majority-of-data-21e0412ca1d3
Tufekci, Z. (2014). Engineering the public: Big data, surveillance and computational politics. First Monday, 19(7). https://doi.org/10.5210/fm.v19i7.4901
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